Module: Single-Cell Analysis
This module will guide you through single-cell transcriptomics analysis in Galaxy.
Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies
An introduction to scRNA-seq data analysis (Lecture)
| Description: | Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq. Slides created by Mehmet Tekman. |
| Length: | 20 minutes |
| Captions: | Mehmet Tekman |
| Created: | 15 February 2021 |
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Slides:
Filter, plot and explore single-cell RNA-seq data (Scanpy) (Demo)
| Description: | You’ve done all the work to make a single cell matrix, with gene counts and mitochondrial counts and buckets of cell metadata from all your variables of interest, now it’s time to fully process our data, to remove low quality cells, to reduce the many dimensions of data that make it difficult to work with, and ultimately to try to define our clusters and to find our biological meaning and insights! |
| Length: | 30 minutes |
| Captions: | Wendi Bacon |
| Created: | 15 February 2021 |
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Tutorial:
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Demo Video
| Description: | This tutorial demonstrates the what barcodes in your sequencing data are, and how to handle them during analysis. Please work through the full tutorial yourself to get all the details. |
| Length: | 10 minutes |
| Captions: | Alireza Khanteymoori |
| Created: | 18 March 2021 |
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| Supported Servers: | Known Working |
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Demo Video
| Description: | 10x genomics has provided not only a cost-effective high-throughput solution to understanding sample heterogeneity at the individual cell level, but has defined the standards of the field that many downstream analysis packages are now scrambling to accommodate. Please work through the full tutorial yourself to get all the details. |
| Length: | 19 minutes |
| Captions: | Pavankumar Videm |
| Created: | 19 May 2023 |
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| Supported Servers: | Known Working Possibly Working |
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Demo Video
| Description: | In this tutorial, we will investigate clustering of single-cell data from 10x Genomics, including preprocessing, clustering and the identification of cell types via known marker genes, using Scanpy. It will be illustrated using a dataset of Peripheral Blood Mononuclear Cells (PBMC), containing 2,700 single cells. Please work through the full tutorial yourself to get all the details. |
| Length: | 45 minutes |
| Captions: | Hans-Rudolf Hotz |
| Created: | 18 March 2021 |
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Tutorial Video
| Description: | Bulk RNA-seq data contains a mixture of transcript signatures from several types of cells. We wish to deconvolve this mixture to obtain estimates of the proportions of cell types within the bulk sample. To do this, we can use single cell RNA-seq data as a reference for estimating the cell type proportions within the bulk data. In this tutorial, we will use bulk and single-cell RNA-seq data, including matrices of similar tissues from different sources, to illustrate how to infer cell type abundances from bulk RNA-seq. |
| Length: | 20 minutes |
| Captions: | Mehmet Tekman, Wendi Bacon |
| Created: | 8 March 2021 |
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Comparing inferred cell compositions using MuSiC deconvolution
This is a self-study session. Please work through the materials on your own, and ask the instructors for help if you get stuck or have any questions!
| Tutorial: | Comparing inferred cell compositions using MuSiC deconvolution |
| Material: | GTN Tutorial |
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Case Study
Demo Video
| Description: | This tutorial will take you from raw FASTQ files to a cell x gene data matrix in AnnData format. What’s a data matrix, and what’s AnnData format? Well you’ll find out! Importantly, this is the first step in processing single cell data in order to start analysing it. |
| Length: | 30 minutes |
| Captions: | Wendi Bacon |
| Created: | 15 February 2021 |
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| Supported Servers: | Known Working |
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Demo Video
| Length: | 11 minutes |
| Captions: | Graeme Tyson |
| Created: | 9 May 2023 |
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Demo Video
| Description: | You’ve done all the work to make a single cell matrix, with gene counts and mitochondrial counts and buckets of cell metadata from all your variables of interest, now it’s time to fully process our data, to remove low quality cells, to reduce the many dimensions of data that make it difficult to work with, and ultimately to try to define our clusters and to find our biological meaning and insights! |
| Length: | 30 minutes |
| Captions: | Wendi Bacon |
| Created: | 15 February 2021 |
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Tutorial Video
| Length: | 15 minutes |
| Captions: | Julia Jakiela |
| Created: | 11 April 2023 |
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| Supported Servers: | Known Working |
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Removing the effects of the cell cycle
This is a self-study session. Please work through the materials on your own, and ask the instructors for help if you get stuck or have any questions!
| Tutorial: | Removing the effects of the cell cycle |
| Description: | Single-cell RNA sequencing can be sensitive to both biological and technical variation, i which is why preparing your data carefully is an important part of the analysis. You want the results to reflect the interesting differences in expression between cells that relate to their type or state. Other sources of variation can conceal or confound this, making it harder for you to see what is going on. One common biological confounder is the cell cycle. Cells express different genes during different parts of the cell cycle, depending on whether they are in their growing phase (G1), duplicating their DNA (the S or Synthesis phase), or dividing in two (G2/M or Mitosis phase). If these cell cycle genes are having a big impact on your data, then you could end up with separate clusters that actually represent cells of the same type that are just at different stages of the cycle. In this tutorial, we will identify the genes whose expression is known to vary during the cell cycle so that we can use them to regress out (or remove) the effects of the cell cycle on the clustering. |
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